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---
base_model:
- stabilityai/stable-diffusion-3.5-medium
tags:
- art
license: other
license_name: stabilityai-ai-community
license_link: LICENSE
---
# Bokeh 3.5 Medium
<div align="center">
<img src="show.jpg" alt="00205_" />
</div>
Bokeh 3.5 Medium is based on **Stable Diffusion 3.5 Medium** as its foundation model, using a 5M high-resolution open-source dataset that underwent rigorous quality and **aesthetic screening** for post-training, ensuring **excellent image quality**, **high fidelity of natural images**, preservation of fine **details**, and enhanced **controllability**.
This model is released under the Stability Community License.
For more details, visit [Tensor.Art](https://tensor.art) or [TusiArt](https://tusiart.com) to explore additional resources and useful information.
## Overview
- Continued training on **SD3.5M**, utilizing carefully curated high-resolution training data to achieve excellent image quality.
- Trained with mixed short/long natural language captions.
- **Short Captions:** Focus on the core subject content of the image.
- **Long Captions:** Provide broader descriptions of the scene environment and atmosphere.
- **Recommended Resolutions:**
`1920x1024`, `1728x1152`, `1152x1728`, `1280x1664`, `1440x1440`
- Powerful customized **fine-tuning performance** that can be widely used for **downstream production tasks**.
- Powerful customized **fine-tuning performance** that can be widely used for **downstream production tasks**.
- Achieve **8~10step** image generation through strong distillation technology, with high-resolution images generated in just 5 seconds on a 3090-level GPU with some quality loss. You can use the [8steps lora](bokeh_8steps_turboX_lora.safetensors) with the base checkpoint or use the [8step checkpoint](bokeh_8steps_turboX.safetensors).
## Advantages
### 🖼️ High-Quality Image Generation
- **State-of-the-art visual fidelity** with improved detail extraction and **aesthetic consistency**.
- **Enhanced resolution support** up to **200W pixels**, ensuring highly detailed image outputs.
- **Carefully curated dataset** ensures better composition, lighting, and overall artistic appeal.
### 🎯 Powerful Custom Fine-Tuning
- **Exceptional LoRA training support**, making it highly effective for:
- Photography
- 3D Rendering
- Illustration
- Concept Art
### ⚡ Efficient Inference & Training
- **Low hardware requirements for inference:**
- **Medium model:** 9GB VRAM (without T5)
- **Full weights inference:** 16GB VRAM (suitable for local deployment)
- **LoRA fine-tuning VRAM requirement:** 12GB - 32GB
## Known Issues
- **Potential human anatomy inconsistencies.**
- **Limited ability to generate photorealistic images.**
- **Some concepts may suffer from aesthetic quality issues.**
## Prompting Guide
### Use a structured prompt combining:
- **Main subject** (e.g., `"Close-up of a macaw"`)
- **Detailed features** (e.g., `"vivid feathers, sharp beak"`)
- **Background environment** (e.g., `"dimly lit environment"`)
- **Atmospheric description** (e.g., `"soft warm lighting, cinematic mood"`)
- **Optimal token length:** **30-70 tokens**.
## Example Output
Using diffusers：
```python
import torch
from diffusers import StableDiffusion3Pipeline
pipe = StableDiffusion3Pipeline.from_pretrained("tensorart/bokeh_3.5_medium", torch_dtype=torch.bfloat16)
pipe = pipe.to("cuda")
image = pipe(
"Close-up of a macaw, dimly lit environment",
num_inference_steps=28,
guidance_scale=4,
height=1920,
width=1024,
negative_prompt="anime,cartoon,bad hands,extra finger，blurred,text,watermark",
negative_prompt_3=""
).images[0]
image.save("macaw.jpg")
```
Using comfyui:
To use this workflow in **ComfyUI**, download the JSON file and load it:
[Base Model Workflow](bk_workflow.json)
[8steps-TurboX Workflow](bokeh_turboX.json)
### 🔧 Training Tools
- **Kohya_ss:** [GitHub Repository](https://github.com/bmaltais/kohya_ss.git)
- **Simple Tuner:** [GitHub Repository](https://github.com/bghira/SimpleTuner)
## Contact
* Website: https://tensor.art https://tusiart.com
* Developed by: TensorArt
* 
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